The present disclosure relates generally to predictive modeling systems for HVAC equipment and more particularly to a predictive modeling system which combines the output of multiple predictive models to form a combined model prediction.
Modern energy conservation measures (ECM) include optimizing the dispatch of a central energy facility to use equipment at optimal times of the day when efficiencies are higher, combined with adding thermal energy storage to reduce the peak energy demand during the day. In order to verify the effectiveness of an ECM, a baseline model is typically created to find the initial cost of running the central energy facility. This cost is then compared to the cost of an optimized plant with new equipment added, and the capital savings is the difference between these numbers. Performance contracts rely on the ability to accurately create the baseline model, as well as the projected savings.
Equipment models can be used to optimize the performance of the central energy facility. This optimization be performed in two ways: (1) as a planning tool to run what if scenarios for capacity planning to see what would happen under different conditions and (2) as a real time operational tool to optimize the current running conditions. Both of these scenarios can use HVAC equipment models to predict power consumption and maintain flow, temperature, power, and pressure load balances.
Existing predictive modeling systems use a single model to predict the performance of HVAC equipment under various scenarios. Efforts to improve model prediction accuracy in the HVAC domain have focused on trying to find a best fitting and generalizable model that works on a wide variety of HVAC equipment of a particular type (e.g., a generalizable chiller model). Although generalizable models perform adequately under most operating conditions, no predictive model is perfect, no matter how complex. Accordingly, even the best predictive models can lack accuracy under some operating conditions. It would be desirable to predict HVAC equipment performance in a manner that overcomes the disadvantages associated with existing predictive modeling systems.